Advanced preoperative assessment of meningiomas

脑膜瘤的高级术前评估

• 批准号: 9755633
• 负责人: Matthew Christopher Murphy
• 金额: $ 35.78万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9755633
• 关键词: Acoustic Neuroma; Address; Adherence; Algorithms; Arachnoid mater; Benign; Brain; Cells; Clinic; Clinical; Collaborations; Complex; Counseling; Data; Detection; Development; Disease; Edema; Environment; Equilibrium; Excision; Goals; Health; Image; Imaging Techniques; Incidence; Intervention; Intracranial Neoplasms; Knowledge; Lesion; Light; Location; Machine Learning; Magnetic Resonance Elastography; Magnetic Resonance Imaging; Maps; Measures; Methods; Network-based; Neurosurgeon; Noise; Operative Surgical Procedures; Outcome; Patients; Performance; Pituitary Gland Adenoma; Probability; Procedures; Property; Publishing; Research; Resolution; Risk; Sampling; Skull Base Neoplasms; Surgeon; Symptoms; Testing; Time; Tissues; Training; Translating; United States; Work; artificial neural network; base; clinical practice; effective therapy; experience; experimental study; heuristics; human subject; imaging approach; imaging modality; improved; magnetic field; mechanical properties; meningioma; multimodality; neural network; neural network architecture; neurosurgery; predictive modeling; simulation; surgical risk; tool; treatment choice; treatment strategy; tumor

PROJECT SUMMARY Meningiomas, which arise from arachnoid cells, make up >1/3 of all intracranial tumors. While typically benign, these tumors induce clinical symptoms due to mass effect and peritumoral edema. In cases requiring intervention, gross total resection provides the best outcomes when possible. However, treatment strategy is ultimately decided by determining the proper balance between surgical difficulty and the patient's overall health. Two mechanical properties are important predictors of surgical difficulty: tumor stiffness and adherence to surrounding tissues. Knowledge of these properties before surgery would allow clinicians to more accurately assess surgical risk and identify the most effective treatment strategy. Mechanical properties are difficult to predict by conventional imaging approaches, but can be directly assessed by Magnetic Resonance Elastography (MRE) and related Slip Interface Imaging (SII). In published studies, we have shown that MRE-based stiffness estimates are significantly correlated with tumor stiffness in meningiomas and pituitary adenomas. Furthermore, SII accurately predicted tumor adherence in meningiomas and vestibular schwannomas. Still, challenges remain to make these findings clinically-impactful. For estimating stiffness, the primary limitation lies in resolution. Therefore, in Aim 1 we will develop a voxel- wise classifier of tumor stiffness. This aim will build on our recently published neural network-based inversion (NNI), which has demonstrated superior performance to conventional direct inversions in simulation and in the brain. In Aim 1a, we will advance NNI by implementing more complex neural network architectures and creating more realistic simulations for training. In Aim 1b, the advances will be validated in a phantom with inhomogeneous stiffness. Finally, in Aim 1c with the aid of our Neurosurgery collaborators, we will collect a large sample of surgical stiffness assessments. We will use these assessments to train a voxel-wise stiffness classifier, which will then be validated in a separate test set. This aim will result in a map that conveys both stiffness and confidence in the prediction on a scale that is clinically meaningful to surgeons. The most-pressing limitations in SII include the subjective interpretation of the images and the lack of spatially resolved predictions. Aim 2 will address these challenges by developing a voxel-wise slip interface classifier. In Aim 2a, we will investigate a neural network-based predictor of slip interfaces to add to our current methods. In Aim 2b, we will evaluate if this new method can improve predictions in phantom experiments. In Aim 2c, we will again leverage surgical assessments of meningioma adherence to train and test a voxel-wise classifier. The result of this aim will be a map of tumor adherence represented as an easily interpreted probability. Taken together, these aims will provide neurosurgeons with clinically-important information to improve patient management. More broadly, technical advances made in this project will impact the entire MRE field.

项目总结 脑膜瘤起源于蛛网膜细胞，占所有颅内肿瘤的1/3。虽然通常是良性的， 由于肿块效应和瘤周水肿，这些肿瘤会引起临床症状。在需要的情况下 在可能的情况下，如果采取干预措施，大体全切除可提供最佳结果。然而，治疗策略是 最终通过在手术难度和患者整体健康状况之间找到适当的平衡来决定 健康。两个力学特性是手术难度的重要预测指标：肿瘤僵硬和粘连 到周围的组织。手术前对这些特性的了解将使临床医生能够更准确地 评估手术风险并确定最有效的治疗策略。机械性能很难预测。 通过常规成像方法，但可以直接通过磁共振弹性成像进行评估 (MRE)和相关的滑动界面成像(SII)。在已发表的研究中，我们已经表明基于MRE的僵硬 在脑膜瘤和垂体腺瘤中，估计值与肿瘤硬度显著相关。 此外，SII准确地预测了脑膜瘤和前庭神经鞘瘤的肿瘤粘连。不过， 要使这些发现在临床上产生影响，仍然存在挑战。 对于刚度的估计，主要的限制在于分辨率。因此，在目标1中，我们将开发一个体素- 肿瘤硬度的WISE分类器。这个目标将建立在我们最近发表的基于神经网络的反演的基础上 (NNI)，在仿真和实际应用中表现出了优于传统直接反演的性能。 大脑。在目标1a中，我们将通过实施更复杂的神经网络结构和 为培训创建更逼真的模拟。在目标1b中，这些进展将在模体中得到验证 刚度不均匀。最后，在Aim 1c中，在我们神经外科合作者的帮助下，我们将收集 手术僵硬评估的大样本。我们将使用这些评估来训练体素方向的僵硬 分类器，然后将在单独的测试集中进行验证。这一目标将产生一幅既能传达这两种信息的地图 在对外科医生有临床意义的量表上的预测的僵硬和信心。 SII中最紧迫的限制包括对图像的主观解释和缺乏空间 已解决预测。Aim 2将通过开发体素滑移界面分类器来解决这些挑战。在……里面 目标2a，我们将研究一种基于神经网络的滑移界面预报器，以补充我们现有的方法。在……里面 目的2b，我们将评估这种新方法是否可以改善模体实验中的预测。在Aim 2c中，我们将 再次利用脑膜瘤粘连的外科评估来训练和测试体素分类器。这个 这一目标的结果将是一张肿瘤粘连的地图，表示为一个容易解释的概率。 综合起来，这些目标将为神经外科医生提供临床上重要的信息，以改善患者 管理层。更广泛地说，该项目取得的技术进步将影响整个MRE领域。